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Synthetic Data Generation Jobs (NOW HIRING)

Humble Robotics

Simulation Engineer

San Francisco, CA · On-site

$100 - $140/hr

You'll build the systems and tools that support closed-loop model testing, synthetic data generation, and sensor simulation. This work will also heavily with modern neural rendering approaches:

Allen Control Systems

Platform Engineer, Data

Austin, TX · On-site

$113K - $136K/yr

You'll partner closely with our ML engineers to orchestrate ingestion, synthetic data generation, and versioned releases, ensuring that every dataset is not only high-integrity and available but ...

Allen Control Systems

Platform Engineer, Data

Austin, TX · On-site

$113K - $136K/yr

You'll partner closely with our ML engineers to orchestrate ingestion, synthetic data generation, and versioned releases, ensuring that every dataset is not only high-integrity and available but ...

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Synthetic Data Generation information

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$31K

$93.2K

$169K

How much do synthetic data generation jobs pay per year?

As of Sep 6, 2026, the average yearly pay for synthetic data generation in the United States is $93,198.00, according to ZipRecruiter salary data. Most workers in this role earn between $54,500.00 and $144,500.00 per year, depending on experience, location, and employer.

What is synthetic data generation?

Synthetic data generation is the process of creating artificial datasets that mimic real-world data. This technique is used to supplement or replace actual data for purposes such as machine learning, software testing, and research, especially when real data is scarce, sensitive, or costly to obtain. Synthetic data can help improve model accuracy, protect privacy, and enable innovation by providing diverse and unbiased datasets. It is commonly used in fields like healthcare, finance, and autonomous vehicles.

What are the key skills and qualifications needed to thrive in synthetic data generation?

To excel in a Synthetic Data Generation role, you need a solid background in computer science, statistics, and data science, often supported by a relevant degree and experience in machine learning. Familiarity with tools such as Python, TensorFlow, PyTorch, and synthetic data generation platforms, as well as knowledge of privacy-preserving techniques, is typically required. Strong problem-solving abilities, creativity, and effective communication set top performers apart in this field. These skills and qualities are crucial for creating high-quality, realistic synthetic datasets that support robust AI model development while safeguarding sensitive information.

What are the main challenges faced by professionals working in synthetic data generation, and how can they be addressed?

Professionals in synthetic data generation often encounter challenges such as ensuring the generated data accurately represents real-world scenarios while maintaining privacy and data security. Balancing realism with anonymization is crucial, especially when synthetic data is used for AI model training or testing. Collaboration with data scientists, domain experts, and privacy officers is common to validate data utility and compliance with regulations. Staying current with advances in generative models and data validation techniques also helps address these challenges and contributes to career growth in this rapidly evolving field.

What is the difference between Synthetic Data Generation vs Data Analyst?

AspectSynthetic Data GenerationData Analyst
Required CredentialsKnowledge of data science, programming, and data privacyDegree in statistics, data science, or related field
Work EnvironmentData science teams, research labs, tech companiesBusiness environments, analytics teams, consulting firms
Industry UsageAI development, machine learning, data privacyBusiness insights, reporting, decision-making
Search & Comparison IntentUnderstanding data generation techniques, privacy solutionsAnalyzing data, generating reports, insights

While Synthetic Data Generation focuses on creating artificial data for privacy and model training, Data Analysts interpret existing data to provide business insights. Both roles require data-related skills but serve different purposes within the data ecosystem.

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What cities are hiring for Synthetic Data Generation jobs?

Cities with the most Synthetic Data Generation job openings:

What states have the most Synthetic Data Generation jobs?

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What job categories do people searching Synthetic Data Generation jobs look for?

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Synthetic Data Generation jobs near you
Infographic showing various Synthetic Data Generation job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 85% Full Time, 11% Part Time, and 3% Contract. Highlights an 86% Physical, 3% Hybrid, and 11% Remote job distribution, with an average salary of $93,198 per year, or $44.8 per hour.

Lead Test Data Management Engineer

Huntington

Columbus, OH • On-site, Remote

Full-time

Posted 12 days ago

Huntington National Bank rating

8.1

Company rating: 8.1 out of 10

Based on 174 frontline employees who took The Breakroom Quiz

67th of 175 rated banks

Job description

Description

The Lead Test Data Management Engineer is responsible for translating Huntington's Test Data Management (TDM) strategy into operational capabilities. This role serves as the bridge between architecture, engineering, governance, and delivery teams, driving the design, implementation, and adoption of enterprise test data capabilities that reduce reliance on production data in lower environments.


The position combines technical leadership, solution design, and hands-on engineering expertise to establish repeatable test data services, accelerate pilot delivery, and create the foundation for a scalable Test Data Management capability.

Basic Qualifications:

  • Bachelor's Degree
  • 5+ years of related experience or an additional 4 years of related work experience may be considered in lieu of the Bachelor's Degree

Platform & Capability Leadership

  • Partner in defining the TDM roadmap and engineering priorities while providing technical leadership for implementation.
  • Identify opportunities to eliminate production data usage in non-production environments.
  • Provide analysis and recommendations for build-vs-buy evaluations, technology tradeoffs, and implementation decisions.

Engineering & Solution Delivery

  • Design and implement synthetic data generation and test data provisioning solutions.
  • Build reusable automation, data pipelines, engineering patterns, and accelerators.
  • Integrate TDM capabilities with testing, quality engineering, and CI/CD processes.
  • Partner with application teams to onboard use cases and validate solutions.
  • Evaluate and implement tooling to improve test data quality, availability, and efficiency.
  • Maintain deep technical expertise in TDM tools, synthetic data technologies, and supporting platforms.

Test Data Lifecycle Management

  • Define and operationalize end-to-end test data lifecycle processes.
  • Establish repeatable provisioning, refresh, archival, and retirement patterns.
  • Define service models for requesting, generating, delivering, and maintaining test data.

Data Quality, Reuse & Governance

  • Establish reusable synthetic data patterns and data asset libraries.
  • Develop standards for referential integrity and realistic test data creation.
  • Define validation approaches to ensure synthetic data is fit for purpose.
  • Measure and report progress toward reducing or eliminating production data usage.

Adoption & Organizational Enablement

  • Support onboarding, training, and adoption activities across technology teams.
  • Monitor emerging industry trends and recommend improvements to Huntington's TDM capabilities.
  • Establish reusable engineering patterns and operational processes that support future scalability.

Additional Experience

  • Experience designing and implementing technology or data solutions.
  • Experience with databases, APIs, data pipelines, automation frameworks, and software delivery practices.
  • Demonstrated ability to lead complex technical initiatives across multiple teams and stakeholders.
  • Experience with synthetic data generation, masking, tokenization, virtualization, or privacy-preserving data techniques.
  • Experience implementing automated test data provisioning solutions.
  • Familiarity with Agile delivery models, DevOps practices, and CI/CD integration.
  • Experience evaluating and implementing technology platforms and vendor solutions.
  • Strong understanding of data governance, risk management, compliance, and information security principles.
  • Experience supporting large-scale application development and testing environments.
  • Ability to balance strategic planning with hands-on technical execution.


Exempt Status: (Yes= not eligible for overtime pay) (No= eligible for overtime pay)

Yes

Workplace Type:

Office

Our Approach to Office Workplace Type

Certain positions outside our branch network may be eligible for a flexible work arrangement. We're combining the best of both worlds: in-office and work from home. Our approach enables our teams to deepen connections, maintain a strong community, and do their best work. Remote roles will also have the opportunity to come together in our offices for moments that matter. Specific work arrangements will be provided by the hiring team.

Huntington is an Equal Opportunity Employer.

Tobacco-Free Hiring Practice: Visit Huntington's Career Web Site for more details.

Note to Agency Recruiters: Huntington Bank will not pay a fee for any placement resulting from the receipt of an unsolicited resume. All unsolicited resumes sent to any Huntington Bank colleagues, directly or indirectly, will be considered Huntington Bank property. Recruiting agencies must have a valid, written and fully executed Master Service Agreement and Statement of Work for consideration.

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